research papers Deformable complex network for refining low-resolution X-ray structures ISSN 1399-0047
Chong Zhang,a Qinghua Wangb and Jianpeng Mab,c* a
Received 29 July 2015 Accepted 16 August 2015
Edited by Z. Dauter, Argonne National Laboratory, USA Keywords: deformable complex network; low-resolution X-ray refinement; homology modeling. Supporting information: this article has supporting information at journals.iucr.org/d
Applied Physics Program, Rice University, Houston, TX 77005, USA, bVerna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA, and c Department of Bioengineering, Rice University, Houston, TX 77005, USA. *Correspondence e-mail:
[email protected]
In macromolecular X-ray crystallography, building more accurate atomic models based on lower resolution experimental diffraction data remains a great challenge. Previous studies have used a deformable elastic network (DEN) model to aid in low-resolution structural refinement. In this study, the development of a new refinement algorithm called the deformable complex network (DCN) is reported that combines a novel angular network-based restraint with the DEN model in the target function. Testing of DCN on a wide range of low-resolution structures demonstrated that it constantly leads to significantly improved structural models as judged by multiple refinement criteria, thus representing a new effective refinement tool for low-resolution structural determination.
1. Introduction It is often a challenge to refine the atomic structures of macromolecular assemblies owing to their weak diffraction of X-rays. In order to build better structural models based on limited-resolution experimental data, it is desirable to introduce additional restraints such as the conventional stereochemical potential (Engh & Huber, 1991). In recent studies, following the development of elastic network models (ENMs; Tirion, 1996; Hinsen, 1998; Atilgan et al., 2001; Stember & Wriggers, 2009), Schro¨der and coworkers proposed a deformable elastic network (DEN) method (Schro¨der et al., 2007, 2010) for better structural refinement. The DEN method utilizes ‘reference structures’ from homology models (Qian et al., 2007; Sˇali & Blundell, 1993) and a series of virtual ‘springs’ between randomly selected atom pairs with variable equilibrium lengths to guide the refinement process. In principle, any structure with reasonable quality that bears some similarity to the target model (the one to be refined) could be used as a reference structure. Compared with conventional refinement, the DEN method delivered substantial improvements for a wide range of low-resolution structures. However, the DEN method only incorporated one-dimensional information on distances between atom pairs, and neglected potentially valuable information from higher dimensions as well as the interdependence of pairs owing to the interaction of more than two atoms, thus limiting the performance of refinement. To address the weakness in the DEN method in refining macromolecular structures, in this work we introduce a deformable complex network (DCN) method that combines DEN with additional information obtained from a deformable angular network (DAN). While DEN defines virtual ‘springs’ between selected atom pairs in the reference model (Schro¨der et al., 2007), the DAN defines harmonic angles formed by
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http://dx.doi.org/10.1107/S139900471501528X
Acta Cryst. (2015). D71, 2150–2157
research papers randomly selected atom triplets. Each atom in a triplet is subject to an angular bending potential. The resultant target function used for refinement includes experimental X-ray diffraction data, the conventional stereochemical potential and the DCN energy that combines DAN and DEN. DCN is deformable owing to the deformability of both the angular part (DAN) and the distance part (DEN). The direction of deformation at a certain refinement step is determined based on the current configuration of the target structure together with the reference structure. The three parameters , and wDCN, where and control the rate of deformation and wDCN is the weight of the DCN restraint, are determined by a three-dimensional grid search with the lowest Rfree factor of the final structure as an indicator of the best choice. Two sets of tests have been used to evaluate the performance of the DCN method. The first set is the refinement of a high-resolution structure of tobacco PR-5d protein (PDB entry 1aun; Koiwa et al., 1999) at three lower resolutions using its homology model from the plant antifungal protein osmotin (PDB entry 1pcv; Min et al., 2004) as the reference structure. The deposited 1aun structure serves as the ‘true answer’ and enables additional assessments of the refined structural models based on multiple criteria besides the Rfree value (Bru¨nger, 1992), such as the all-atom root-mean-square ˚) deviation (r.m.s.d.), the global distance test (GDT) (
